Making learning systems practical

Part 1 Scaling up: Initializing neural networks using decision trees, Arunava Banerjee Recurrent neural networks with continuous topology adaptation, Kalman filter based training, Dragan Obradovic Imposing bounds on the number of categories for incremental concept formation, Leon Shklar, Haym Hirsh Scaling to domains with irrelevant features, Patrick Langley, Stephanie Sage. Part 2 Robust and efficient learning: Mixture models for learning from incomplete data, Zoubin Ghahramans, Russell Greiner Supervised learning using labelled and unlabelled examples, Geoffrey Towell Abnormal data points in the data set - an algorithm for robust neural net regression, Yong Liu Dynamic modelling of chaotic time series by neural networks, Gustavo Deco, Bernd Schurmann Fast distribution-specific learning, Dale Schurmans, Russell Greiner. Part 3 Improving and analyzing generalization: Exploring the decision forest - an empirical investigation of Occam's razor in decision tree induction, Patrick M. Murphy, Michael J. Pazzani N-learners problem - system of PAC learners, Nageswara S.V. Rao, E.M. Oblow The discriminative power of a dynamic model neuron, Anthony M. Zador, Barak A. Pearlmutter On learning the neural network architecture - a case study, Mostefa Golea Probabilistic self-structuring and learning, David Garvin, Peter Rayner A practical approach to evaluating generalization performance, Marjorie Klenin. Part 5 Real world applications: What makes derivational analogy work - an experience report using APU, Sanjay Bhansali, Mehdi T. Harandi Learning verb translation rules from ambiguous examples and a large semantic hierarchy, Hussein Almuallim et al Efficient learning of regular expressions from approximate examples, Alvis Brazma A comparison of RBF and MLP networks for classification of biomagnetic fields, Martin F. Schlang et al Fast perceptual learning of motion in humans and neural networks, Lucia M. Vaina et al.